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Phil. Trans. R. Soc. B (2009) 364, 1491–1498
doi:10.1098/rstb.2009.0006
Eco-evolutionary dynamics: disentangling
phenotypic, environmental and
population fluctuations
Thomas H. G. Ezard1,*, Steeve D. Côté2 and Fanie Pelletier3,4
1
Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK
Département de Biologie and Centre d’études Nordiques, Université Laval, 1045,
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
3
NERC Centre for Population Biology, Imperial College London, Silwood Park Campus,
Ascot, Berkshire SL5 7PY, UK
4
Département de biologie, Université de Sherbrooke, 2500 boul. de l’Université,
Sherbrooke, Québec J1K 2R1, Canada
2
Decomposing variation in population growth into contributions from both ecological and
evolutionary processes is of fundamental concern, particularly in a world characterized by rapid
responses to anthropogenic threats. Although the impact of ecological change on evolutionary
response has long been acknowledged, the converse has predominantly been neglected, especially
empirically. By applying a recently published conceptual framework, we assess and contrast the
relative importance of phenotypic and environmental variability on annual population growth in five
ungulate populations. In four of the five populations, the contribution of phenotypic variability was
greater than the contribution of environmental variability, although not significantly so. The
similarity in the contributions of environment and phenotype suggests that neither is worthy of
neglect. Population growth is a consequence of multiple processes, which strengthens arguments
advocating integrated approaches to assess how populations respond to their environments.
Keywords: eco-evolutionary dynamics; ecology; evolution; phenotype; population
1. INTRODUCTION
Although the link between natural selection and
demography has long been recognized, only recently
have biologists begun to appreciate that ecological and
evolutionary changes can occur on the same time scale
(Thompson 1998; Sinervo et al. 2000; Yoshida et al.
2003; Hairston et al. 2005; Hanski & Saccheri 2006;
Kinnison & Hairston 2007; Pelletier et al. 2007a).
The traditional view argued that natural selection
determines which phenotypes persist or go extinct,
while density-dependent and stochastic factors
determine population growth (Saccheri & Hanski
2006). Population biologists have typically ignored
the potential feedback of change in phenotypic
distribution on population processes (Slobodkin
1961). Many techniques in evolutionary ecology
assume environmental consistency ( Falconer &
Mackay 1996). The assumption of environmental
constancy has recently been challenged by several
studies, demonstrating that, under certain circumstances, evolution can occur on contemporary time
scales (Hendry & Kinnison 1999; Kinnison & Hendry
2001) and that evolutionary processes can have
quantifiable effects on ecological dynamics (Sinervo
et al. 2000; Yoshida et al. 2003; Hairston et al. 2005;
* Author for correspondence ([email protected]).
Electronic supplementary material is available at http://dx.doi.org/10.
1098/rstb.2009.0006 or via http://rstb.royalsocietypublishing.org.
One contribution of 14 to a Theme Issue ‘Eco-evolutionary dynamics’.
Pelletier et al. 2007a). It is therefore necessary to
consider ecological variation in evolutionary studies, as
well as evolutionary responses in ecological studies, to
understand comprehensively the interplay between
these processes (Fussmann et al. 2007).
A first step in understanding the interactions
between ecology and evolution is to explore the links
between different levels of biological organization
(Pelletier et al. 2009). At the population level, the
fundamental processes of birth and death link natural
selection and population dynamics. It is therefore
necessary to consider the possibility of an ecoevolutionary feedback between phenotypic traits and
demography (Ricklefs & Wikelski 2002; Coulson et al.
2006). This feedback arises because a change in any
environmental variable can alter selective pressures.
One consequence is a new phenotype distribution,
which in turn affects density that then affects (partially)
all subsequent phenotype distributions (Coulson et al.
2006; Kokko & Lopez-Sepulcre 2007). Applying an
evolutionary demography approach to reanalyse the
exceptional long-term sequence of beak shape in a
Darwin’s finch species (Geospiza fortis) on Galapagos,
Hairston et al. (2005) showed that an adaptive response
in beak size contributed twice as much to variation in
population size as ecological processes. A few notable
exceptions apart (Hairston et al. 2005; Pelletier
et al. 2007a), the effect of changes in phenotypic
trait distributions on ecological processes has rarely
been investigated under non-laboratory conditions
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T. H. G. Ezard et al.
Interdependent dynamics in populations
(Saccheri & Hanski 2006). As an illustration, it is not
known how environmental conditions, phenotypic
change and population dynamics are influenced by
concurrent selection or how emergent changes determine
their dynamics. Initial attempts to address this topic
might therefore ask: does annual population growth vary
the most as a direct result of environmental change, or is it
more strongly related to phenotypic variability?
One way to address this question empirically is to
explore how a trait distribution in 1 year influences
subsequent vital rates (here, survival and recruitment),
and hence population growth. The association between
a trait distribution and population growth is also
probably affected by environmental conditions
(Pelletier et al. 2007a) and/or predation pressure
(Yoshida et al. 2003; Jones et al. 2009). There is a
need to quantify the interplay between phenotypic
distributions and population growth across a range of
environmental conditions and model species. Using
longitudinal monitoring (between 16 and 33 years) of
five ungulate populations exposed to disparate environmental conditions, the aim of this paper is to disentangle
the effects of environmental and phenotypic changes on
population dynamics. An essential step in the assessment of population fluctuations is to understand how
the dynamics of heritable phenotypic traits influence
population processes (Coulson & Tuljapurkar 2008).
Evaluating the relative importance of different processes
on population growth is fundamental to population and
evolutionary biology, especially given the increasing
number of studies showing rapid phenotypic changes to
anthropogenic threats (reviewed by Parmesan & Yohe
2003; Bradshaw & Holzapfel 2006; Gienapp et al. 2008;
Hendry et al. 2008).
2. CONCEPTUAL FRAMEWORK
We begin by outlining Hairston et al.’s (2005) framework for comparing ecological and evolutionary
dynamics, before progressing to discuss the details of
its application here. Hairston et al. (2005) aimed to
compare ‘ecological and evolutionary dynamics’. Their
rationale was that temporal changes in some attribute
of population dynamics—say population growth from
1 year to the next, hereafter simply population
growth—are the result of temporal changes in
ecological and evolutionary processes. Expressed
mathematically, this statement is
dX
vX dk vX dz
Z
C
;
dt
vk dt
vz dt
where X is the attribute of population dynamics; k is an
ecological variable; and z is an evolutionary variable.
Hairston et al (2005) considered summary statistics
(e.g. mean beak shape) as evolutionary variables and
demonstrated applications in discrete and continuous
time. The discrete-time analogue of the continuous
expression above is
Xðt C hÞKXðtÞ Z
vX
vX
Dk C
Dz C/ ;
vk
vz
where h is some interval of time between consecutive
measures. Our explanation of the framework is
restricted to the discrete case, since it represents the
Phil. Trans. R. Soc. B (2009)
data collection protocol of the study populations as well
as their biology (they are the so-called ‘birth-pulse’
populations). Application requires three steps:
(i) derive statistical relationships between X and z
independent of k and between X and k independent
of z, (ii) calculate changes in z and k across the time
interval, and (iii) compare the contributions of z and k
to X. More than one z and k can be incorporated in a
multivariate version, but we do not expand
that argument due to our aim of application across
multiple systems.
Ecological and evolutionary variables are rather
broad terms; changes in either result from multiple
underlying causes. Changes in ‘evolutionary variables’
(as defined by Hairston et al. 2005) are not necessarily
examples of evolutionary dynamics. Suppose that z is a
phenotype. Changes to z across an interval might result
from, for example, phenotypic plasticity (Pigliucci
2001; Nussey et al. 2005a,b) or changes in the age
structure of the population (Coulson & Tuljapurkar
2008). Henceforth, we refer to z as phenotypic and k as
environmental variables rather than evolutionary and
ecological, respectively. No attempt is made to
decompose causes of observed variation in z and k in
this first empirical application of the framework. To
reiterate, the aim here is to apply the method and hence
disentangle the effects of changes in the environment
and phenotype distributions on population dynamics.
3. MATERIAL AND METHODS
(a) Study populations
The data used here are taken from five longitudinal studies of
marked, long-lived ungulates in temperate areas of the
Northern Hemisphere. Although these species have broadly
similar life history, their dynamics fluctuate widely (figure l)
and they occupy very different habitats. The Soay sheep
population (Ovis aries) lives on a Hebridean island devoid of
trees, whereas forests with isolated meadows dominate the
habitat of roe deer (Capreolus capreolus) in Trois-Fontaines,
France. The three Canadian populations (two bighorn
sheep (Ovis canadensis) and one mountain goat (Oreamnos
americanus)) occupy alpine regions of the Rockies.
A summary of available data and key references is given in
table 1; further information on data collection protocols is
given in the electronic supplementary material 1. We define
population growth from year t to tC1 as the number of
individuals in the study area in year tC1 divided by the
corresponding number in year t.
Mass early in life is considered generally to be a key
phenotypic trait (Albon et al. 1987; Clutton-Brock et al. 1987;
Metcalfe & Monaghan 2001) with a heritable component
(Coltman et al. 2005; Wilson et al. 2005). Birth weight is an
important determinant of neonatal survival in several species
of mammals (Albon et al. 1987; Clutton-Brock 1991; Côté &
Festa-Bianchet 2001), and early conditions can have lasting
effects on multiple fitness components (Lummaa & CluttonBrock 1998; Lindström 1999). Data on mass early in life,
which we refer to as ‘juvenile mass’, are available in all
populations except bighorn sheep at Sheep River. At Sheep
River, chest circumference at six months of age is available
and correlates very highly with autumn weight (rZ0.90,
p!0.001, Pelletier et al. 2005). Juvenile mass values were
adjusted for date of capture in a given year using regression
techniques in all populations.
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Interdependent dynamics in populations
abundance
300
200
abundance
(b)
150
50
abundance
(c)
300
200
(d ) 160
abundance
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Pacific Decadal Oscillation (PDO, Zhang et al. 1997) were
correlated with decreases in population growth in bighorn
sheep, whereas high values of sward height (a vegetation
index; Crawley et al. 2004) correlated with increases in
population growth in Soay sheep. A PDO of, say, K4 was
therefore reflected to be C4 in analyses. This process ensured
that increasing environmental variables correlated with
increasing population growth in all populations.
(a)
120
80
(e)
140
abundance
T. H. G. Ezard et al.
100
60
1970
1980
1990
year
2000
2010
Figure 1. Although all five ungulate populations are of similar
life history, their population dynamics vary markedly. Time
series are shown for the periods of each population analysed.
Study populations: (a) Soay sheep; (b) bighorn sheep at Ram
Mountain; (c) roe deer; (d ) mountain goats; and (e) bighorn
sheep at Sheep River.
As different research groups have monitored different
study sites, they have different sampling protocols (table 1).
All populations are furthermore influenced to various extents
by different environmental variables. For the purposes of our
analysis, we used the environmental variable identified as the
best correlate of annual growth in each population.
Environmental variables, where high values indicate harsh
conditions, were reflected around zero for ease of (statistical
and visual) comparison. For example, high values of the
Phil. Trans. R. Soc. B (2009)
(b) Statistical analysis
The first step in application of the framework is to derive
coefficients for expected population growth following
observed phenotypic and environmental changes. These
coefficients were obtained from generalized linear models
( Fox 2002). Different populations required different models:
coefficients for the Soay sheep and both the bighorn sheep
populations were estimated using standard linear models.
Standard linear models were not appropriate for the roe deer
and mountain goat populations since the variance around
population growth increased nonlinearly with the mean.
Gamma-type models (where variance increases as a function
of the mean2), but with an identity link function, were used
for these populations. Additionally, density at Ram Mountain
( Festa-Bianchet et al. 1998) and predator presence in both
the bighorn sheep populations ( Festa-Bianchet et al. 2006)
were controlled for. Significant outliers (identified using
‘cook’s distance’; Fox 2002, p. 206) were removed to achieve
diagnostic plots that did not reveal systematic bias (see the
electronic supplementary material 1).
Once the coefficients have been determined, they can be
used to hypothesize the effect of environmental or phenotypic
change on population growth. Since different populations
have different dynamics (figure 1), and therefore different
types of within-population variations, mixed-effect models
were used to assess these effects statistically. Mixed-effect
models are appropriate tools for analysis when some level of
structure—e.g. experimental units or repeated measurements
on experimental subjects—is apparent in the data (so-called
grouped data; see Pinheiro & Bates 2000). Mixed-effect
models consist of fixed and random effects. Fixed effects are
parameters associated with global trends, modelling patterns
of variation common to all experimental units (Pinheiro &
Bates 2000, p. 3). Random effects model the correlation in
residuals caused by the structure of the data (Diggle et al.
2002, p. 82). In the simplest case, different experimental
subjects are distinguished using different intercepts. In more
complex cases, the residuals in the different experimental
subjects might vary systematically and be modelled by
different slopes and different intercepts. If random slopes
are necessary, then the responses to changes in the
explanatory variables vary in direction between the experimental subjects. Here, the five populations are the
experimental subjects. Modelling the correlation in residuals
(using random effects) is critical here because the number of
experimental subjects is small (5) and less than the number
of observations per subject (mean 23.4) (Diggle et al. 2002).
It is also consistent with our motivation of assessing
differences in the relative importance of environmental and
phenotypic fluctuations across the five populations.
There are five ways of combining phenotypic and
environmental variability as fixed effects (both with
interactions, both without interactions, phenotype only,
environment only and neither), and five ways of combining
phenotype and environmental variability as random effects
(population-level variability in gradients for both, for environment only, for phenotype only, in intercepts only and not at all).
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T. H. G. Ezard et al.
Interdependent dynamics in populations
Table 1. Summary of study populations, with dates between extreme measurements of population growth (as observed annual
population growth, X ), environmental variables (k), phenotypic variables (z) and key references for further information.
(Commas indicate two separate series of years when data requirements were met. If no date is given when mass was measured,
the data were collected from individuals within 48 hours of birth. Note that all study populations used regression methods to
estimate birth mass (except Sheep River, see §3). INDVI, integrated normalized difference vegetation index; PDO, Pacific
Decadal Oscillation. The biological impacts of these environmental variables are discussed at length in the appropriate
references. Further details on these populations can be found in the electronic supplementary material 1.)
population
location
Soay sheep
St Kilda,
Scotland
Caw Ridge,
Canada
mountain goats
roe deer
bighorn sheep
bighorn sheep
number
of years X
16
9, 8
Trois-Fontaines, 21
France
Ram Mountain, 18, 12
Canada
Sheep river,
33
Canada
k
lt (Aug) Sward height in March
birth mass (Clutton-Brock et al.
(Crawley et al. 2004)
1992)
kid mass on 15 July (Côté &
lt ( Jun) maximum rate of increase in
Festa-Bianchet 2001)
INDVICstanding INDVI
(Pettorelli et al. 2007)
lt (Mar) June rainfall (Gaillard et al. 1997) birth mass (Gaillard et al. 1993)
lt (May) PDO (Zhang et al. 1997); density mass on 5 June ( Festa-Bianchet
( Festa-Bianchet et al. 1998)
et al. 1996)
chest circumference at six
lt (Mar) PDO2 (Zhang et al. 1997);
months of age (Pelletier et al.
presence of predators
2005)
( Festa-Bianchet et al. 2006)
Akaike Information Criterion (AIC). Information criteria
(Burnham & Anderson 2002) provide a compromise between
number of parameters used and model likelihood. The model
with most support has the lowest AIC value. Models within
two AIC values of the minimum AIC value have ‘substantial’
support, and only when this difference is greater than four does
the model’s support become ‘considerably less’ (Burnham &
Anderson 2002, p. 71). We also calculated Akaike weights
(Burnham & Anderson 2002), which can be interpreted as the
likelihood of a particular model being the best, given the set of
models used. Another interpretation of model weight is that it
quantifies the weight of evidence in favour of a particular model
(Burnham & Anderson 2004). We do not calculate AIC values
for models without random effects, since comparisons are only
reasonable between models with and without random effects if
they have the same fixed effects. This comparison was made
using likelihood ratio tests.
0.5
0.4
mean absolute change
z
0.3
0.2
0.1
0
SSSK
BHRM
RDTF
MGCR
BHSR
population
Figure 2. Mean absolute change of environment (black bars)
and phenotype (white bars) with 95% parametric confidence
intervals for each population, calculated following eqn (8) by
Hairston et al. (2005). The change quantifies the effects on
population growth of environmental and phenotypic changes,
respectively. Absolute rates are presented since changes in
environmental and phenotypic variables can be negative as well
as positive. Study population codes: SSSK, Soay sheep;
BHRM, bighorn sheep at Ram Mountain; RDTF, roe deer;
MGCR, mountain goats; BHSR, bighorn sheep at Sheep River.
These combinations of explanatory variables were regressed
against population growth (differenced to remove temporal
autocorrelation) across the same time interval. The model with
most support was chosen from the complete set of potential
models ( Whittingham et al. 2006) and differentiated using the
Phil. Trans. R. Soc. B (2009)
(c) Individual-based method comparison
The Soay sheep population was previously the subject of an
individual-based analysis, which found that the distribution
of weights in the population contributed significantly to the
impact that each individual makes to annual population
growth (Pelletier et al. 2007a). Pelletier et al.’s (2007a)
approach was a retrospective decomposition of population
growth into contributions from phenotype. Hairston et al.
(2005) calculated hypothetical changes in population growth
from observed phenotypic variability. Although both methods
attempt to do very different things, they are linked. When
there is viability or fertility selection on a trait, then Pelletier
et al.’s (2007a) method will lead to observed population
growth being strongly influenced by the distribution of the
trait value. This selection (assuming principally that traits are
heritable and phenotypic plasticity is low) will generate a
change in the mean value of the trait over a time step. This is
how Hairston et al.’s (2005) framework defines phenotypic
change. To illustrate the ability of Hairston et al.’s (2005)
framework to approximate results from more involved
methods, we regressed results from the framework applied
here against the individual-based one (see fig. 2c in Pelletier
et al. 2007a). A quasibinomial model (with logit link function)
was used since contribution to population growth is bounded
between 0 and 1 ( Fox 2002).
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Interdependent dynamics in populations
T. H. G. Ezard et al.
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Table 2. Akaike Information Criterion (AIC, with model weights in brackets) for all possible models. AIC values are given to one
decimal place, AIC weights to three decimal places. (Each row and column combination represents one model, e.g. the cell [2,2]
(the AIC is K31.3, the model weight is 0.013) is for a model containing global effects of k and z and population-level variability
in gradients and intercepts for z. The minimum AIC value denotes the model with most support (denoted in bold), which
contains effects of environmental (k) and phenotypic (z) changes as well as their interaction. This model does not differ
significantly (according to likelihood ratio tests) from one without random intercepts.)
global effect of k, z and
their interaction
global effect of k and z
global effect of z only
global effect of k only
no global effect
population-level variability
in gradients and intercepts
for both k and z
population-level varia- population-level varia- population-level
bility in gradients and bility in gradients and variability in intercepts only
intercepts for k only
intercepts for z only
K28.9 (0.004)
K34.9 (0.077)
K34.9 (0.077)
K38.9 (0.567 )
K25.3 (0.001)
K16.4 (0.000)
K25.9 (0.001)
K17.5 (0.000)
K31.3 (0.013)
3.0 (0.000)
K31.9 (0.017)
4.5 (0.000)
K31.3 (0.013)
K22.4 (0.000)
K31.9 (0.017)
K23.5 (0.000)
K35.3
K1.0
K35.9
1.5
individual-based
0.04
0.02
0
– 0.4
– 0.2
0
0.2
0.4
0.6
population-based
Figure 3. The correlation between the Hairston et al. (2005)
population-based method and Pelletier et al.’s (2007a)
individual-based method was fairly strong: years when
mean birth weight decreases the most are also years when
the summed individual contributions to population growth
are the largest in the Soay sheep population. The methods
differ markedly, being linked by a chain of responses (see
main text for details). The individual-based method is
quantified as the summed contribution of juvenile mass to
population growth; the Hairston et al. (2005) method is the
absolute rate of change in mean juvenile mass.
Mixed-effect models were fitted using the lmer function in
the lme4 package (v. 0.999375-24, Bates 2005) in the
R environment (v. 2.7.1, R Development Core Team 2008).
The Laplacian approximation to maximum likelihood was used.
4. RESULTS
Phenotypic and environmental changes were of similar
orders of magnitude: phenotypic change had a slightly
greater effect in four of the five populations (the
exception being mountain goats), but was not statistically significant (using one- or two-tailed t- or
Wilcoxon signed-rank tests; figure 2). The Soay sheep
population fluctuated the most with environmental and
phenotypic changes: absolute rates of year-to-year
change were three times greater in this population
than in the others (figure 2). The ratio of phenotypic to
Phil. Trans. R. Soc. B (2009)
(0.093)
(0.000)
(0.123)
(0.000)
environmental change was the lowest for mountain
goats (0.48) and varied between 1.45 (bighorn sheep at
Sheep River) and 1.22 for the remainder.
The model with the most support featured global
effects of environmental and phenotypic changes as
well as their interaction. This model had the lowest
AIC value, but a model weight of only 0.567 (table 2).
A model that featured environmental change only also
had some support (DAIC was 3, model weight 0.123)
and is especially worthy of consideration given that it is
more parsimonious, containing two fewer parameters.
The relative similarity in directional trends of changes
in population size with changes in phenotype and
environment (see the electronic supplementary
material 2) means that there were no statistical reasons
to differentiate regression slopes (table 2) or intercepts
(likelihood ratio test: p-value on c23:5!10 K9 ;1 O0.05)
between the five populations.
The conceptual framework applied here was
compared with a more involved method that linked
phenotypic traits to population growth at the individual
level, applied previously to the Soay sheep population.
The summed individual contributions of juvenile mass
to annual population growth correlated significantly
with the phenotypic rates of change estimated here
(on the scale of the logit link function: bZK2.975,
s.e.Z0.971, p!0.05, r 2Z0.687; figure 3). We stress
again that the two approaches attempt to do very
different things (see §§3 and 5 for more).
5. DISCUSSION
In this paper, we applied a framework that aims to
disentangle phenotypic, environmental and population
fluctuations. The framework quantifies a link between
phenotype, environment and some measure of population performance. Our results suggest that
(i) phenotypic and environmental variability make
statistically indistinguishable contributions to population growth (figure 2), (ii) the model for population
growth with the most support featured environmental
and phenotypic changes as well as their interaction as
explanatory variables (table 2; see the electronic
supplementary material 2), and (iii) the method
correlated fairly well with a more data-intensive,
individual-based approach (figure 3).
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Is the overall similarity in trends between environmental and phenotypic variables with population
growth to be expected, given the broadly similar life
history of the case studies analysed? Possibly, although
the five populations experience different climatic
regimes, they have variable age at first breeding
(between 1 and 4), variable generation times (between
4 and 9) and highly contrasting population dynamics
(figure 1). The Soay sheep population has the lowest
age at first breeding and generation time, and fluctuates
substantially around the population’s average trajectory
(figure 1a), all of which are potential reasons for the
differences in mean absolute rates of phenotypic and
environmental changes (figure 2). We do not explore
these patterns further due, in part, to the relatively low
number of populations analysed. The most likely
model (given those assessed) for annual population
growth contains phenotypic and environmental
changes as well as their interaction (figure 2). The
support for this model strengthens the argument that
phenotypic and environmental distributions fluctuate
on a similar time scale, which was the pivot of Hairston
et al.’s (2005) framework. These fluctuations were
statistically identical across the five populations
(table 2; see the electronic supplementary material 2).
The feedback from the population on the environmental variables is negligible, except for the Soay sheep
on St Kilda where sward height (Crawley et al.
2004) could conceivably show an evolutionary
response to grazing pressure. Eco-evolutionary feedbacks are clearly important in many systems (Post &
Palkovacs 2009), as is variable plasticity in phenotypic
responses (Pigliucci 2001), but we followed Hairston
et al. (2005) in assuming here that evolutionary
responses in environmental variables used in our
study are negligible.
Drastic phenotypic change might not result in
changes in population growth. For example, the
bighorn sheep populations have experienced evolutionary change in horn size and body weight in response to
trophy hunting (Coltman et al. 2003, 2005). Juvenile
mass is known to affect juvenile survival and therefore
population growth in ungulates and mammals (Albon
et al. 1987; Clutton-Brock et al. 1987; Metcalfe &
Monaghan 2001). Our analyses only considered the
weight distribution in one demographic class, which
might hamper our ability to detect an eco-evolutionary
feedback (Post & Palkovacs 2009). The extent to which
juvenile mass affects population growth differs between
populations: the r 2 values of models used to calculate
the hypothetical changes in z and k ranged between
0.574 for Soay sheep and 0.163 for bighorn sheep at
Sheep River. Although the same trait is used throughout, its impact is not uniform across the five
populations; neither is the impact of the environmental
variables. Inclusion of additional phenotypes or
environmental variables could decrease the unexplained variation since an individual is neither defined
by one trait alone nor affected by a singular environmental variable. Different populations are probably
affected by different combinations of phenotypic traits,
which would hamper our aim of quantifying the effects
of phenotypic and environmental changes across
multiple contrasting populations.
Phil. Trans. R. Soc. B (2009)
In the univariate case, does the inability to detect
population-level variability in how phenotypic variability affects population growth (table 2), despite
differences in effect magnitude (figure 2), suggest that
similarity in evolutionary direction (Schluter et al.
2004) plays a critical role in the impact of juvenile mass
on population growth? Although mean absolute rates of
change were similar (figure 2), changes in population
size are predicted more accurately by environmental
fluctuations: the massive change in model likelihood
when environment is removed suggests a lack of
predictive power based on phenotype alone (table 2).
A model only featuring environmental fluctuations had
non-negligible model weight (table 2). The phenotypic
change might mirror (or exaggerate) environmental
fluctuations (Pelletier et al. 2007b), yet be a relatively
poor predictor of population dynamics. Changes in
phenotype are not independent of changes in environment and might arise from, for example, phenotypic
plasticity (Pigliucci 2001). Future developments could
perhaps extend this framework to a hierarchical
approach that links phenotype, demography and
population growth (Coulson et al. 2003).
It is uncontested that alternative approaches can
yield contrasting results. Comparison of data-intensive
approaches with those that do not rely on individualbased data remains valuable. Pelletier et al. (2007a)
linked phenotypes at the individual level to variation in
annual population growth and found that body weight
contributed up to 18 per cent of total variation in
annual population growth of the Soay sheep population. Thus, in similar fashion to Hairston et al.
(2005), Pelletier et al. (2007a, p. 1571) concluded that
‘there is substantial opportunity for evolutionary
dynamics to leave an ecological signature and vice
versa’. The correlation between their individual-based
method and the framework applied here was fairly
strong: years when mean birth weight decreases the
most are also years when the summed individual
contributions to population growth are the largest
(figure 3). When food is scarce, density is often high
(Crawley et al. 2004), which causes decreases in
individual condition and consequently increased
mortality (Pelletier et al. 2007a). Assuming that birth
weight is a trait under heritable selection and low
phenotypic plasticity, it is anticipated that selection will
be stronger when environmental conditions are harsh
(Wilson et al. 2006). The trait then contributes more to
population growth (Pelletier et al. 2007a). This will
generate a change in the mean value of the trait over a
time step, which, coupled with the change in population growth, will give a large absolute value of vX/vz
in Hairston et al.’s (2005) conceptual framework. As
mentioned previously, no attempt is made here to
decompose causes of observed variation in population
growth into its environmental or phenotypic drivers, or
to consider the effect of environmental change on
phenotypic expression. The method of Hairston et al.
(2005) does not directly characterize evolutionary
dynamics and uses hypothetical rather than observed
changes in population size. It neither considers the
differences between individuals that are a prerequisite
for evolutionary change nor those between different
parts (e.g. age and sex classes) of the population that
Downloaded from http://rstb.royalsocietypublishing.org/ on June 18, 2017
Interdependent dynamics in populations
are masked by taking averages. It summarizes many
processes, but can nevertheless elucidate populationlevel trends where exhaustive data are unavailable.
The conceptual framework proposed by Hairston
et al. (2005) has prompted an explosion (Hanski &
Saccheri 2006; Saccheri & Hanski 2006; Kinnison &
Hairston 2007; Pelletier et al. 2007a) of interest in how
ecological and evolutionary processes interact. This
first published application of their method emphasizes
the importance of considering how populations
respond to changes in their environmental and
phenotypic distributions. There was insufficient
evidence to determine whether phenotypic or environmental fluctuations contributed more, in the statistical
sense, to annual population growth (figure 2; table 2).
Does the similarity hold across the entire life-history
spectrum? Differences across a range of species, life
histories and climates in population-level consequences of phenotypic and environmental variability
could prove insightful for demographic inference
generally, including conservation applications. Hairston
et al.’s (2005) framework is a simplified, but nevertheless
useful, tool to quantify environmental and phenotypic
impact on population dynamics because it enables
analysis in less data-rich systems than those used here.
Our results stress the importance of developing and
applying approaches that reflect the interdependence of
environmental, phenotypic and population dynamics.
The authors would like to thank Luca Börger, Tim CluttonBrock, Tim Coulson, Marco Festa-Bianchet, Jean-Michel
Gaillard, Andrew Hendry, Michael Kinnison, Josephine
Pemberton and an anonymous reviewer for making data
available and providing comments that markedly improved
earlier drafts. T.H.G.E. is funded by a Natural Environment
Research Council grant NE/E015956/1 to Andy Purvis and
Paul Pearson. F.P. is funded by the NERC Centre for
Population Biology and a grant from the Natural Sciences
and Engineering Research Council of Canada. Finally,
we want to thank the funding agencies, logistical
support networks and, especially, the many volunteers on
the five populations.
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